Journal of Mathematical Imaging and Vision

, Volume 44, Issue 3, pp 449–462 | Cite as

Implicitly Weighted Methods in Robust Image Analysis

  • Jan Kalina


This paper is devoted to highly robust statistical methods with applications to image analysis. The methods of the paper exploit the idea of implicit weighting, which is inspired by the highly robust least weighted squares regression estimator. We use a correlation coefficient based on implicit weighting of individual pixels as a highly robust similarity measure between two images. The reweighted least weighted squares estimator is considered as an alternative regression estimator with a clear interpretation. We apply implicit weighting to dimension reduction by means of robust principal component analysis. Highly robust methods are exploited in tasks of face localization and face detection in a database of 2D images. In this context we investigate a method for outlier detection and a filter for image denoising based on implicit weighting.


Robustness High breakdown point Outlier detection Robust correlation analysis Template matching Face recognition 



This research is fully supported by the project 1M06014 of the Ministry of Education, Youth and Sports of the Czech Republic. The author is grateful to two anonymous referees for providing valuable suggestions.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Center of Biomedical InformaticsInstitute of Computer Science of the Academy of Sciences of the Czech RepublicPragueCzech Republic

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